Jointness of growth determinants

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance, jointness reveals generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct but mutually reinforcing effects. Negative jointness implies that explanatory variables are substitutes and capture similar underlying effects. In a cross‐country dataset we show that jointness among 67 determinants of growth is important, affecting inference and informing economic policy. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting Singapore's quarterly GDP with monthly external trade

In this paper we suggest a methodology to formulate a dynamic regression with variables observed at different time intervals. This methodology is applicable if the explanatory variables are observed more frequently than the dependent variable. We demonstrate this procedure by developing a forecasting model for Singapore's quarterly GDP based on monthly external trade. Apart from forecasts, the model provides a monthly distributed lag structure between GDP and external trade, which is not possible with quarterly data.     

Censored regression quantiles with endogenous regressors

This paper develops a semiparametric method for estimation of the censored regression model when some of the regressors are endogenous (and continuously distributed) and instrumental variables are available for them. A “distributional exclusion” restriction is imposed on the unobservable errors, whose conditional distribution is assumed to depend on the regressors and instruments only through a lower-dimensional “control variable,” here assumed to be the difference between the endogenous regressors and their conditional expectations given the instruments. This assumption, which implies a similar exclusion restriction for the conditional quantiles of the censored dependent variable, is used to motivate a two-stage estimator of the censored regression coefficients. In the first stage, the conditional quantile of the dependent variable given the instruments and the regressors is nonparametrically estimated, as are the first-stage reduced-form residuals to be used as control variables. The second-stage estimator is a weighted least squares regression of pairwise differences in the estimated quantiles on the corresponding differences in regressors, using only pairs of observations for which both estimated quantiles are positive (i.e., in the uncensored region) and the corresponding difference in estimated control variables is small. The paper gives the form of the asymptotic distribution for the proposed estimator, and discusses how it compares to similar estimators for alternative models.     

联立方程模型的工具变量方法质疑与商榷

本文质疑联立方程模型前定变量的工具变量性质：前定变量并不保证与当期行为解释变量的相关性,由其构建的工作回归元所完成的分阶段最小二乘估计因而并非两阶段最小二乘估计。建议按照简约式方程构建工作回归元,其具有模型数理逻辑支持下的可替代意义。工作回归元的不同导致结构式方程分阶段最小二乘估计的不同结果,之于恰好识别方程则揭示了业内关于间接最小二乘估计方法的一个误区。     

Research Investment and the Economic Returns To Chinese Agricultural Research

This article measureseconomic returns to research investment in Chinese agricultureusing the production function approach. A stock-of-knowledgevariable constructed from the past research investment is directlyincluded in the production function as an explanatory variablein the production function. Improved rural infrastructure, irrigation,and education are also included as explanatory variables to avoidthe upward bias in the estimates of returns to agricultural research.A two-way variable coefficients technique is used in the estimationto reduce estimation biases due to the remaining measurementand omitted variables problems. Sensitivity analyses are conductedto test the effects of various lag structures on the return estimates.The results show that rates of return to research investmentin Chinese agriculture are high, ranging from 36% to 90% in1997, and the rates are increasing over time.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

Economic Crime as Hiding Behavior

The article introduces "hiding behavior" as a concept for understanding why people conceal economic problems and solve them by committing crimes. This concept's theoretical purport, empirical applicability, and relevance for the research area are considered through an analysis of an empirical case. An explanatory model is then developed, focusing on the concrete social relations in which hiding behavior originates and in which it remains embedded. From case studies we see how hiding behavior is situated in social relations characterized by a specific form of uncertainty arising from engulfment and isolation. In these relations money plays a central role, providing a medium for action-coordination essential in the incidence of economic crime. Finally, the explanatory power of the proposed conceptual model is discussed, with several possible refinements suggested for it.     

中国货币条件指数构建及与经济增长的实证分析

货币条件指数作为反映一个国家货币政策松紧程度的指标,自1994年由加拿大银行首次提出后,引起了国际社会的普遍关注。本文选取1998年1季度～2011年3季度间共55个季度的实际利率、实际有效汇率、实际信贷规模缺口作为解释变量,选取实际产出与潜在产出之间的缺口作为被解释变量,使用单方程估计法,通过构造总需求曲线,利用最小二乘法计算各解释变量的权重,得出中国的实际货币条件指数。并通过分析对比中国汇率、利率、信贷规模变动与宏观经济增长之间的关系,验证中国实际货币条件指数对实体经济变动的解释能力。     

货物周转量与经济发展关系研究

分析了货物周转量的主要影响因素,以6个经济指标作为解释变量,通过相关性分析,得到各指标变量与货物周转量的相关系数;同时运用主成分分析,构造了货物周转量与国内生产总值(GDP)之间的线性回归模型;并对建国以来我国货物周转量同GDP关系的阶段性特征予以分析。数学模型实证检验结果表明:各个经济发展阶段,货物周转量同GDP均呈现线性关系,并且随着经济的发展,单位GDP增长所需的货物周转量呈现出减小的趋势。     

Variable selection and functional form uncertainty in cross-country growth regressions

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We present a new framework that makes such a joint treatment possible, using flexible nonlinear models specified by Gaussian process priors and addressing the variable selection problem by means of Bayesian model averaging. Using this framework, we extend the linear model to allow for parameter heterogeneity of the type suggested by new growth theory, while taking into account the uncertainty in selecting explanatory variables. Controlling for variable selection uncertainty, we confirm the evidence in favor of parameter heterogeneity presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections.     

The occurrence of outliers in the explanatory variable considered in an errors-in-variables framework

Summary The problem of estimating the slope of a linear relationship between two jointly normally distributed random variables is considered when outliers may occur in the explanatory variable. It will be studied as a special case of an errors-in-variables problem where the explanatory variable is measured which a nonnormally distributed error. In this more general model and under certain conditions a consistent estimator can be given with a normal limiting distribution. Applications to cases of outliers in the explanatory variable will be presented.     

Semi‐parametric Regression under Model Uncertainty: Economic Applications

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross‐country growth regression.     

Raising the bar (5)

Raising the bar (5). Spatial Economic Analysis. This editorial summarizes and comments on the papers published in this issue 12(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper examines the impact of the level of education on the decision to migrate and finds that it is approximately twice as large if both variables are modelled simultaneously. The second paper is one of the first papers to introduce a spatial component to models of international environmental agreements and to develop an exciting overlap with New Economic Geography. The third paper provides a tool, applied to Beijing, with which urban economic planners can investigate the role of variation and selection mechanisms in cluster development and identify possible paths of growth. The fourth paper contributes to the existing literature on retail geography by examining the role of consumption possibilities as an urban amenity. The fifth paper develops a Bayesian estimator of a linear regression model with spatial lags among the dependent variable, the explanatory variables and the disturbances. Finally, the sixth paper develops a semi-parametric generalized method of moments (GMM) estimator for a spatial autoregressive model with space-varying coefficients of the explanatory variables and a spatial autoregressive coefficient common to all units.     

The Problem of Time Dependent Explanatory Variables at the Context-Level in Discrete Time Multilevel Event History Analysis: A Comparison of Models Considering Mobility Between Local Labour Markets as an Example

The paper deals with the question of how to include time dependent explanatory variables at the context-level in multilevel event history models. In general, context-level explanatory variables in multilevel models are assumed to be time constant. Only time constant context-level explanatory variables perform the task of reducing context-level error variance. Thus, it will be suggested that the analysis should be extended to a three-level model. In this model, time periods of persons constitute level 1 units, time periods of contexts constitute level 2 units and the contexts themselves constitute level 3 units – in which in turn level 2 units are clustered. Considering mobility between local labour markets as an example, four different ways of modelling time varying context-level variables are compared. The result is that the proposed three-level model leads to the most conservative results.     

Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior But Cannot Completely Explain the Syndrome

Abstract . An empirical analysis of the property crimes, robbery, burglary and larceny, is presented for all 120 counties in Kentucky. While this analysis is based on an economic model of crime, certain sociological and legal variables are included as well in the system of equations. Overall, the empirical results support prior studies’findings with the exception that a quadratic relationship is found to exist between urbanization and each of the property crimes. Furthermore, neither the economic nor the non economic influences measured appear more important for affecting crime rates. Specifically, results indicate that the level of poverty, the degree of tourism, the presence of police, the unemployment rate and the apprehension rate all affect property crimes. In contrast, the length of sentence, the degree of industrialization, the level of public assistance payments and the proportion of youth in the county have no affect on property crime rates in these areas.     

Local tax interaction and endogenous spatial weights based on quality of life

This paper studied the role of municipal quality of life as a driver of strategic tax interactions among local governments. A number of studies point out the existence of strategic interactions using spatial econometric models in which the spatial weights are mostly based on non-economic criteria or basic economic variables such as per capita gross domestic product (GDP). We propose the more sophisticated well-being indicator of municipal quality of life as the driver for these interactions. To deal with the potential endogeneity, we rely on instrumental variable estimators. The empirical analysis focuses on the main local tax in Spain (property) and on municipalities with more than 50,000 inhabitants, and it confirms the relevance of differentials in quality of life for the understanding of tax choices.     

Quantile regression for dynamic panel data with fixed effects

This paper studies a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias, we suggest the use of the instrumental variables quantile regression method of Chernozhukov and Hansen (2006) along with lagged regressors as instruments. In addition, we describe how to employ the estimated models for prediction. Monte Carlo simulations show evidence that the instrumental variables approach sharply reduces the dynamic bias, and the empirical levels for prediction intervals are very close to nominal levels. Finally, we illustrate the procedures with an application to forecasting output growth rates for 18 OECD countries.     

Specification diagnostics based on Laguerre alternatives for econometric models of duration

Score tests of the null hypothesis of exponentially distributed durations (conditional on regressors) against alternatives in a family of approximations to arbitrary distributions for non-negative random variables are developed. The test statistics take a simple, easily calculated and interpreted form. The alternatives considered are expansions of various orders in Laguerre polynomials. An economic model generating exponential unemployment duration is presented. The statistics are applied in a look at unemployment durations in the Denver Income Maintenance Experiment data.     

Specification error in multinomial logit models: Analysis of the omitted variable bias

In this article, we analyze the omitted variable bias problem in the multinomial logistic probability model. Sufficient, as well as necessary, conditions under which the omitted variable will not create asymptotically biased coefficient estimates for the included variables are derived. Conditional on the response variable, if the omitted explanatory and the included explanatory variable are independent, the bias will not occur. Bias will occur if the omitted relevant variable is independent with the included explanatory variable. The coefficient of the included variable plays an important role in the direction of the bias.     

Linear regression after selection

We study the effect, upon linear regression, of explicit selection on the dependent variable. If the explanatory variables are multinormally distributed along with the dependent variable, then the regression coefficient vector in the selected population is a scalar multiple of that in the original population.